OSGAN: One-shot distributed learning using generative adversarial networks.

With the advancements in mobile technology, a large amount of data is generated by end devices, which has created a renewed interest in developing new AI-based applications for gaining insights into these data. However, most of these distributed applications need data aggregated at a central server, which has posed severe bandwidth, latency, security, and privacy issues. This paper presents OSGAN (One-Shot distributed learning algorithm using Generative Adversarial Networks), a generic framework that trains a generative adversarial network (GAN) at each client and uses GAN's generative capabilities to create sample data at the server. The server aggregates these data from various clients, builds a deep learning model and sends its parameters back to the clients in one communication round, i.e. the exchange of information between the clients and the server happens only once. In this paper, we present the design and implementation of OSGAN and evaluate its performance by comparing it with the state-of-the-art federated learning (FL) and central training algorithms for both IID and non-IID distribution of data. Our experiments on multiple datasets show that our proposed approach achieves a similar accuracy when compared with both FL and central training algorithms. Specifically, the accuracy drop with OSGAN is maintained within 2% for multiple datasets and multiple numbers of clients. Our results show that the proposed approach reduces the amount of data transfer by almost 98% when compared with federated learning and close to 80% when compared with the central learning approach thereby providing substantial benefit in terms of saving the bandwidth. [ABSTRACT FROM AUTHOR]